A Dynamic Clause Specific Initial Weight Assignment for Solving Satisfiability Problems Using Local Search

Abdelraouf Ishtaiwi; Feda Alshahwan; Naser Jamal; Wael Hadi; Muhammad AbuArqoub

doi:10.3390/a14010012

A Dynamic Clause Specific Initial Weight Assignment for Solving Satisfiability Problems Using Local Search

Algorithms ◽

10.3390/a14010012 ◽

2021 ◽

Vol 14 (1) ◽

pp. 12

Author(s):

Abdelraouf Ishtaiwi ◽

Feda Alshahwan ◽

Naser Jamal ◽

Wael Hadi ◽

Muhammad AbuArqoub

Keyword(s):

Local Search ◽

State Of The Art ◽

Small Neighborhood ◽

Extensive Study ◽

The State ◽

Stochastic Local Search ◽

Dynamic Allocation ◽

Initial Weight ◽

Large Neighborhood ◽

Weight Assignment

For decades, the use of weights has proven its superior ability to improve dynamic local search weighting algorithms’ overall performance. This paper proposes a new mechanism where the initial clause’s weights are dynamically allocated based on the problem’s structure. The new mechanism starts by examining each clause in terms of its size and the extent of its link, and its proximity to other clauses. Based on our examination, we categorized the clauses into four categories: (1) clauses small in size and linked with a small neighborhood, (2) clauses small in size and linked with a large neighborhood, (3) clauses large in size and linked with a small neighborhood, and (4) clauses large in size and linked with a large neighborhood. Then, the initial weights are dynamically allocated according to each clause category. To examine the efficacy of the dynamic initial weight assignment, we conducted an extensive study of our new technique on many problems. The study concluded that the dynamic allocation of initial weights contributes significantly to improving the search process’s performance and quality. To further investigate the new mechanism’s effect, we compared the new mechanism with the state-of-the-art algorithms belonging to the same family in terms of using weights, and it was clear that the new mechanism outperformed the state-of-the-art clause weighting algorithms. We also show that the new mechanism could be generalized with minor changes to be utilized within the general-purpose stochastic local search state-of-the-art weighting algorithms.

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String Kernels for Native Language Identification: Insights from Behind the Curtains

Computational Linguistics ◽

10.1162/coli_a_00256 ◽

2016 ◽

Vol 42 (3) ◽

pp. 491-525 ◽

Cited By ~ 9

Author(s):

Radu Tudor Ionescu ◽

Marius Popescu ◽

Aoife Cahill

Keyword(s):

State Of The Art ◽

Native Language ◽

Extensive Study ◽

The State ◽

Language Identification ◽

Language Transfer ◽

Word Choice ◽

Transfer Effects ◽

String Kernels ◽

Kernel Approach

The most common approach in text mining classification tasks is to rely on features like words, part-of-speech tags, stems, or some other high-level linguistic features. Recently, an approach that uses only character p-grams as features has been proposed for the task of native language identification (NLI). The approach obtained state-of-the-art results by combining several string kernels using multiple kernel learning. Despite the fact that the approach based on string kernels performs so well, several questions about this method remain unanswered. First, it is not clear why such a simple approach can compete with far more complex approaches that take words, lemmas, syntactic information, or even semantics into account. Second, although the approach is designed to be language independent, all experiments to date have been on English. This work is an extensive study that aims to systematically present the string kernel approach and to clarify the open questions mentioned above. A broad set of native language identification experiments were conducted to compare the string kernels approach with other state-of-the-art methods. The empirical results obtained in all of the experiments conducted in this work indicate that the proposed approach achieves state-of-the-art performance in NLI, reaching an accuracy that is 1.7% above the top scoring system of the 2013 NLI Shared Task. Furthermore, the results obtained on both the Arabic and the Norwegian corpora demonstrate that the proposed approach is language independent. In the Arabic native language identification task, string kernels show an increase of more than 17% over the best accuracy reported so far. The results of string kernels on Norwegian native language identification are also significantly better than the state-of-the-art approach. In addition, in a cross-corpus experiment, the proposed approach shows that it can also be topic independent, improving the state-of-the-art system by 32.3%. To gain additional insights about the string kernels approach, the features selected by the classifier as being more discriminating are analyzed in this work. The analysis also offers information about localized language transfer effects, since the features used by the proposed model are p-grams of various lengths. The features captured by the model typically include stems, function words, and word prefixes and suffixes, which have the potential to generalize over purely word-based features. By analyzing the discriminating features, this article offers insights into two kinds of language transfer effects, namely, word choice (lexical transfer) and morphological differences. The goal of the current study is to give a full view of the string kernels approach and shed some light on why this approach works so well.

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Improved Local Search with Momentum for Bayesian Networks Structure Learning

Entropy ◽

10.3390/e23060750 ◽

2021 ◽

Vol 23 (6) ◽

pp. 750

Author(s):

Xiaohan Liu ◽

Xiaoguang Gao ◽

Zidong Wang ◽

Xinxin Ru

Keyword(s):

Local Search ◽

Complex Networks ◽

Bayesian Networks ◽

Structure Learning ◽

Hybrid Methods ◽

State Of The Art ◽

The State ◽

Experimental Results ◽

Local Optimum ◽

Networks Structure

Bayesian Networks structure learning (BNSL) is a troublesome problem that aims to search for an optimal structure. An exact search tends to sacrifice a significant amount of time and memory to promote accuracy, while the local search can tackle complex networks with thousands of variables but commonly gets stuck in a local optimum. In this paper, two novel and practical operators and a derived operator are proposed to perturb structures and maintain the acyclicity. Then, we design a framework, incorporating an influential perturbation factor integrated by three proposed operators, to escape current local optimal and improve the dilemma that outcomes trap in local optimal. The experimental results illustrate that our algorithm can output competitive results compared with the state-of-the-art constraint-based method in most cases. Meanwhile, our algorithm reaches an equivalent or better solution found by the state-of-the-art exact search and hybrid methods.

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Stochastic local search: a state-of-the-art review

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v11i1.pp716-727 ◽

2021 ◽

Vol 11 (1) ◽

pp. 716

Author(s):

Muhamet Kastrati ◽

Marenglen Biba

Keyword(s):

Local Search ◽

State Of The Art ◽

Search Algorithms ◽

Combinatorial Problems ◽

Stochastic Local Search ◽

Local Search Algorithms ◽

Science And Engineering ◽

Short Introduction ◽

Basic Notation ◽

Learning Data

The main objective of this paper is to provide a state-of-the-art review, analyze and discuss stochastic local search techniques used for solving hard combinatorial problems. It begins with a short introduction, motivation and some basic notation on combinatorial problems, search paradigms and other relevant features of searching techniques as needed for background. In the following a brief overview of the stochastic local search methods along with an analysis of the state-of-the-art stochastic local search algorithms is given. Finally, the last part of the paper present and discuss some of the most latest trends in application of stochastic local search algorithms in machine learning, data mining and some other areas of science and engineering. We conclude with a discussion on capabilities and limitations of stochastic local search algorithms.

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CCEHC: An Efficient Local Search Algorithm for Weighted Partial Maximum Satisfiability (Extended Abstract)

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/716 ◽

2017 ◽

Cited By ~ 1

Author(s):

Chuan Luo ◽

Shaowei Cai ◽

Kaile Su ◽

Wenxuan Huang

Keyword(s):

Local Search ◽

State Of The Art ◽

Search Algorithm ◽

Stochastic Local Search ◽

Local Search Algorithm ◽

Maximum Satisfiability ◽

Biased Random Walk ◽

Configuration Checking ◽

Significant Generalization ◽

Max Sat

Weighted partial maximum satisfiability (WPMS) is a significant generalization of maximum satisfiability (MAX-SAT), with many important applications. Recently, breakthroughs have been made on stochastic local search (SLS) for weighted MAX-SAT and (unweighted) partial MAX-SAT (PMS). However, the performance of SLS for WPMS lags far behind. In this work, we present a new SLS algorithm named CCEHC for WPMS. CCEHC is mainly based on a heuristic emphasizing hard clauses, which has three components: a variable selection mechanism focusing on configuration checking based only on hard clauses, a weighting scheme for hard clauses, and a biased random walk component. Experiments show that CCEHC significantly outperforms its state-of-the-art SLS competitors. Experiments comparing CCEHC with a state-of-the-art complete solver indicate the effectiveness of CCEHC on a number of application WPMS instances.

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Scoring Functions Based on Second Level Score for k-SAT with Long Clauses

Journal of Artificial Intelligence Research ◽

10.1613/jair.4480 ◽

2014 ◽

Vol 51 ◽

pp. 413-441 ◽

Cited By ~ 4

Author(s):

S. Cai ◽

C. Luo ◽

K. Su

Keyword(s):

Phase Transition ◽

Computational Complexity ◽

Local Search ◽

State Of The Art ◽

Scoring Function ◽

Experimental Results ◽

Great Success ◽

Stochastic Local Search ◽

Scoring Functions ◽

New Scoring

It is widely acknowledged that stochastic local search (SLS) algorithms can efficiently find models for satisfiable instances of the satisfiability (SAT) problem, especially for random k-SAT instances. However, compared to random 3-SAT instances where SLS algorithms have shown great success, random k-SAT instances with long clauses remain very difficult. Recently, the notion of second level score, denoted as "score_2", was proposed for improving SLS algorithms on long-clause SAT instances, and was first used in the powerful CCASat solver as a tie breaker. In this paper, we propose three new scoring functions based on score_2. Despite their simplicity, these functions are very effective for solving random k-SAT with long clauses. The first function combines score and score_2, and the second one additionally integrates the diversification property "age". These two functions are used in developing a new SLS algorithm called CScoreSAT. Experimental results on large random 5-SAT and 7-SAT instances near phase transition show that CScoreSAT significantly outperforms previous SLS solvers. However, CScoreSAT cannot rival its competitors on random k-SAT instances at phase transition. We improve CScoreSAT for such instances by another scoring function which combines score_2 with age. The resulting algorithm HScoreSAT exhibits state-of-the-art performance on random k-SAT (k>3) instances at phase transition. We also study the computation of score_2, including its implementation and computational complexity.

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Dynamic Initial Weight Assignment for MaxSAT

Algorithms ◽

10.3390/a14040115 ◽

2021 ◽

Vol 14 (4) ◽

pp. 115

Author(s):

Abdelraouf Ishtaiwi ◽

Qasem Abu Al-Haija

Keyword(s):

Real World ◽

State Of The Art ◽

Initial Weight ◽

Maximum Satisfiability ◽

Weight Assignment ◽

Real World Problem ◽

Wide Range ◽

Consistent Solution ◽

Order Of Magnitude ◽

Performance Gains

The Maximum Satisfiability (Maximum Satisfiability (MaxSAT)) approach is the choice, and perhaps the only one, to deal with most real-world problems as most of them are unsatisfiable. Thus, the search for a complete and consistent solution to a real-world problem is impractical due to computational and time constraints. As a result, MaxSAT problems and solving techniques are of exceptional interest in the domain of Satisfiability (Satisfiability (SAT)). Our research experimentally investigated the performance gains of extending the most recently developed SAT dynamic Initial Weight assignment technique (InitWeight) to handle the MaxSAT problems. Specifically, we first investigated the performance gains of dynamically assigning the initial weights in the Divide and Distribute Fixed Weights solver (DDFW+Initial Weight for Maximum Satisfiability (DDFW+InitMaxSAT)) over Divide and Distribute Fixed Weights solver (DDFW) when applied to solve a wide range of well-known unweighted MaxSAT problems obtained from DIMACS. Secondly, we compared DDFW+InitMaxSAT’s performance against three known state-of-the-art SAT solving techniques: YalSAT, ProbSAT, and Sparrow. We showed that the assignment of dynamic initial weights increased the performance of DDFW+InitMaxSAT against DDFW by an order of magnitude on the majority of problems and performed similarly otherwise. Furthermore, we showed that the performance of DDFW+InitMaxSAT was superior to the other state-of-the-art algorithms. Eventually, we showed that the InitWeight technique could be extended to handling partial MaxSAT with minor modifications.

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Practical Picture Processing

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100051700 ◽

1974 ◽

Vol 32 ◽

pp. 338-339

Author(s):

T. A. Welton

Keyword(s):

Radiation Damage ◽

Coherence Length ◽

Spatial Information ◽

State Of The Art ◽

Coherent Radiation ◽

The State ◽

Energy Spread ◽

Electron Micrograph ◽

Picture Processing ◽

Molecular Skeleton

Various authors have emphasized the spatial information resident in an electron micrograph taken with adequately coherent radiation. In view of the completion of at least one such instrument, this opportunity is taken to summarize the state of the art of processing such micrographs. We use the usual symbols for the aberration coefficients, and supplement these with £ and 6 for the transverse coherence length and the fractional energy spread respectively. He also assume a weak, biologically interesting sample, with principal interest lying in the molecular skeleton remaining after obvious hydrogen loss and other radiation damage has occurred.

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